Predicting chattering alarms: A machine Learning approach

Abstract Alarm floods represent a widespread issue for modern chemical plants. During these conditions, the number of alarms may be unmanageable, and the operator may miss safety-critical alarms. Chattering alarms, which repeatedly change between the active and non-active states, are responsible for most of the alarm records within a flood episode. Typically, chattering alarms are only addressed and removed retrospectively (e.g. during periodic performance assessments). This study proposes a Machine-Learning based approach for alarm chattering prediction. Specifically, a method for dynamic chattering quantification has been developed, whose results have been used to train three different Machine Learning models – Linear, Deep, and Wide&Deep models. The algorithms have been employed to predict future chattering behavior based on actual plant conditions. Performance metrics have been calculated to assess the correctness of predictions and to compare the performance of the three models

Learning from Major Accidents: a Machine Learning Approach

A B S T R A C T Learning from past mistakes is crucial to prevent the reoccurrence of accidents involving dangerous sub-stances. Nevertheless, historical accident data are rarely used by the industry, and their full potential is largely unexpressed. In this setting, this study set out to take advantage of improvements in data sci-ence and Machine Learning to exploit accident data and build a predictive model for severity prediction. The proposed method makes use of classification algorithms to map the features of an accident to the corresponding severity category (i.e., the number of people that are killed and injured). Data extracted from existing databases is used to train the model. The method has been applied to a case study, where three classification models - i.e., Wide, Deep Neural Network, and Wide&Deep - have been trained and evaluated on the Major Hazard Incident Data Service database (MHIDAS). The results indicate that the Wide&Deep model offers the best performance.(c) 2022 The Authors. Published by Elsevier Ltd.This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/

Assessment of Safety Barrier Performance in Environmentally Critical Facilities: Bridging Conventional Risk Assessment Techniques with Data-Driven Modelling

The failure of emission control systems in industrial processes undergoing emission regulations can cause severe harm to the environment. In this context, safety engineering principles can be applied to analyze process deviations and identify suitable safety barriers to mitigate harmful emissions during critical events. However, the selection, design, and assessment of proper safety barriers may be complex due to several contingencies such as the inability to perform extensive field tests on systems under strict emission regulations. In this study, an approach is proposed to couple conventional hazard identification techniques with a digital model of a flue gas treatment system to support the identification and performance assessment of safety barriers for emission control. Resilience analysis is used to evaluate the behavior of the most relevant safety barrier options, selected through a screening with conventional hazard identification tools. Barriers are simulated using the digital model of the system, gathering key information for their design and evaluation, and overcoming the limitations to field tests at the real plant. The methodology is illustrated with reference to acid gas removal in waste-to-energy facilities, a relevant example of an emission control system that is typically exposed to significant process deviations

Systematic coarse-graining of environments for the non-perturbative simulation of open quantum systems

Conducting precise electronic-vibrational dynamics simulations of molecular systems poses significant challenges when dealing with an environment composed of numerous vibrational modes. Here, we introduce novel techniques for the construction of effective phonon spectral densities that capture accurately open system dynamics over a finite time interval of interest. When combined with existing non-perturbative simulation tools, our approach can reduce significantly the computational costs associated with many-body open system dynamics

A Machine Learning Approach to Predict Chattering Alarms

The alarm system plays a vital role to grant safety and reliability in the process industry. Ideally, an alarm should inform the operator about critical conditions only, and a set of corrective actions should be associated with each alarm. During alarm floods, the operator may be overwhelmed by several alarms in a short time span. Crucial alarms are more likely to be missed during these situations. Poor alarm management is one of the main causes of unintended plant shut down, incidents and near misses in the chemical industry. Most of the alarms triggered during a flood episode are nuisance alarms –i.e. alarms that do not communicate new information to the operator, or alarms that do not require an operator action. Chattering alarms –i.e. that repeat three or more times in a minute, and redundant alarms –i.e. duplicated alarms, are common forms of nuisance. Identifying nuisance alarms is a key step to improve the performance of the alarm system. Advanced techniques for alarm rationalization have been developed, proposing methods to quantify chattering, redundancy and correlation between alarms. Although very effective, these techniques produce static results. Machine learning appears to be an interesting opportunity to retrieve further knowledge and support these techniques. This knowledge can be used to produce more flexible and dynamic models, as well as to predict alarm behaviour during floods. The aim of this study is to develop a machine learning-based algorithm for real-time alarm classification and rationalization, whose results can be used to support the operator decision-making procedure. Specifically, efforts have been directed towards chattering prediction during alarm floods. Advanced techniques for chattering, redundancy and correlation assessment have been performed on a real industrial alarm database. A modified approach has been developed to dynamically assess chattering, and the results have been used to train three different machine learning models, whose performance has been evaluated and discussed

A Machine Learning Approach to Predict Chattering Alarms

The alarm system plays a vital role to grant safety and reliability in the process industry. Ideally, an alarm should inform the operator about critical conditions only; during alarm floods, the operator may be overwhelmed by several alarms in a short time span. Crucial alarms are more likely to be missed during these situations. Poor alarm management is one of the main causes of unintended plant shut down, incidents and near misses in the chemical industry. Most of the alarms triggered during a flood episode are nuisance alarms –i.e. alarms that do not communicate new information to the operator, or alarms that do not require an operator action. Chattering alarms –i.e. that repeat three or more times in a minute, and redundant alarms –i.e. duplicated alarms, are common forms of nuisance. Identifying nuisance alarms is a key step to improve the performance of the alarm system. Advanced techniques for alarm rationalization have been developed, proposing methods to quantify chattering, redundancy and correlation between alarms. Although very effective, these techniques produce static results. Machine Learning appears to be an interesting opportunity to retrieve further knowledge and support these techniques. This knowledge can be used to produce more flexible and dynamic models, as well as to predict alarm behaviour during floods. The aim of this study is to develop a machine learning-based algorithm for real-time alarm classification and rationalization, whose results can be used to support the operator decision-making procedure. Specifically, efforts have been directed towards chattering prediction during alarm floods. Advanced techniques for chattering, redundancy and correlation assessment have been performed on a real industrial alarm database. A modified approach has been developed to dynamically assess chattering, and the results have been used to train three different machine learning models, whose performance has been evaluated and discussed

A neural network approach to predict the time-to-failure of atmospheric tanks exposed to external fire

Domino scenarios triggered by fire pose severe risks to workers, assets, and the environment. Accurate quantitative models are needed to support mitigation actions addressing the prevention of fire escalation, especially considering sensitive targets such as atmospheric tanks containing large quantities of dangerous substances. A novel approach based on neural networks was developed, allowing the accurate quantification of the time-to-failure (TTF) of atmospheric tanks exposed to external fires accounting for mitigation actions. Data from a lumped parameter model were used to train and assess neural networks' performance. The toolbox of models obtained provides the TTF of atmospheric tanks both in the case of unmitigated fire scenarios and considering safety barriers and protection measures, such as water deluges and fire monitors. Model predictions are fast, accurate, and supplemented with confidence intervals. The comparative analysis demonstrated the better performance of the model developed compared to simplified correlations widely used in the literature to predict TTF. The approach developed, based on the integration of neural networks in consequence analysis tools, shows significant potential for the advancement of a quantitative assessment of domino scenarios, providing accurate and user-friendly tools for a quick evaluation of domino fire scenarios under both mitigated and unmitigated conditions

A machine learning approach to predict chattering alarms

The alarm system plays a vital role to ensure safety and reliability in the process industry. Ideally, an alarm should inform the operator about critical conditions only and provide guidance to a set of corrective actions associated with each alarm. During alarm floods, the operator may be overwhelmed by several alarms in a short time span, and crucial alarms are more likely to be missed during these situations. Most of the alarms triggered during a flood episode are nuisance alarms –i.e. alarms that do not convey any new information to the operator, or alarms that do not require operator actions. Chattering alarms that repeat three or more times in a minute and redundant or duplicated alarms are common forms of nuisance alarms. Identifying such nuisance alarms is a key step to improve the performance of the alarm system. Recently, advanced techniques for alarm management have been developed to quantify alarm chatter; although effective, these techniques produce relatively static results. Machine learning algorithms offer an interesting opportunity to analyse historical alarm data and retrieve knowledge, which can be used to produce more flexible and dynamic models, as well as to predict alarms behaviour. The present study aims to develop a machine learning-based algorithm for chattering prediction during alarm floods. A modified approach based on run lengths distribution has been developed to evaluate the likelihood of future alarm chatter. The method has allowed categorizing historical alarm events as alarms that will (or will not) show chattering in the future. Finally, categorized alarms have been used to train a Deep Neural Network, whose performance has been evaluated against the ability to predict alarm chatter. Overall, the Neural Network has shown good prediction capabilities and most of the chattering alarms were correctly identified